Choose one track. Master’s proposals target Spring 2027. Final scope and programme approval are agreed with the supervisor; bachelor scopes are suggested adaptations.
TRACK 01 · IMPLEMENTATION INCLUDED
Technical track
Develop a working solution and test whether it addresses the problem.
Possible research question
How can an integrated health-information system expose incomplete, outdated or inconsistent data in ways that improve interpretation?
Suggested tasks
Read research on health-data quality across systems and compare existing solutions.
Identify one problem faced by healthcare staff.
Write a research question and define what the solution should do.
Set up two simulated health-record systems with some missing, outdated or conflicting values.
Build a combined view that shows where each value came from and flags problems with units, duplicates and timestamps.
Compare it with a view without these warnings. Test whether users notice data problems and interpret the record correctly.
Explain what worked, what did not, and how the results compare with earlier research.
Evaluation, degree scope and deliverables
Study and evaluation
Compare the implemented solution with a basic integrated view without explicit provenance and quality cues. Combine reproducible technical tests with an appropriate empirical evaluation.
Fault-detection accuracy
Successful integration of valid records
Ability to identify information unsuitable for a decision
Degree scope
Bachelor’s
Connect two simulated sources, implement a few quality checks and build a readable comparison view. Test injected faults and straightforward interpretation tasks.
Master’s
Study how provenance and quality cues affect users’ interpretation, separating integration correctness from decision quality.
Background
Programming
APIs and databases
Data modelling; advanced ML is not required
Possible deliverables
A focused literature review, justified problem and research question
A working prototype with source code and setup instructions
A reproducible comparison and an appropriate study of use
A report explaining design lessons, results and limitations
TRACK 02 · NO IMPLEMENTATION REQUIRED
Non-technical track
Study existing systems, information or work practices. You do not need to develop software.
Possible research question
How do healthcare staff judge the reliability of conflicting information across systems?
Suggested tasks
Read earlier studies of health-data quality across systems.
Choose one problem and write a research question the study can answer.
Review information-quality and interoperability practices for one care workflow.
Use fictional records to explore how staff interpret stale data, conflicting units and missing timestamps.
Map information provenance, workarounds and responsibility for resolving inconsistencies.
Analyse the interviews, observations or documents using a clearly described method. Look for disagreements as well as common patterns.
Explain the findings, compare them with earlier research and suggest practical improvements.
Evaluation, degree scope and deliverables
Study and evaluation
Use a bounded empirical study of health-data quality across systems. Justify case selection, recruitment and the analysis method. Distinguish observed behaviour from participants’ perceptions; use triangulation or a comparison where it serves the research question.
Interpretation of known data-quality faults
Coordination practices and responsibility for correction
Evidence for the findings, conflicting cases and limits of the study
Degree scope
Bachelor’s
Study one case or a small set of existing materials. Agree the interviews, documents or scenario tasks with the supervisor. Describe the method, analyse the findings and give practical recommendations.
Master’s
Use a clear research question and relevant IS theory. Justify the cases, participants and analysis method. Explain what the findings add to earlier research and where they may apply. No software development is required.
Background
Literature review and academic writing
Qualitative or quantitative research methods
Interest in health-data quality across systems; no programming prerequisite
Possible deliverables
A literature review and research question
A study plan and approved research material
An analysis supported by interviews, observations, documents or scenario results
A thesis with findings, recommendations and limitations
Scope and access
Use a small FHIR subset and synthetic records, for example Synthea. Standards-compliant exchange and synthetic testing do not establish clinical validity. These implementation-related limits apply when developing or testing a technical solution. For a non-technical study, agree access to participants or existing materials early, use approved or fictional cases where appropriate, and distinguish perceptions from observed outcomes.
Agree access to data, participants or existing materials and any required ethics or privacy review before committing. A non-technical track needs a systematic study, not a working prototype.
Full academic proposal
Working topic
Making Health Data Quality Visible Across Information Systems
Brief outline
This proposal examines health-data quality across systems in the work and information needs of healthcare staff. The technical track combines a literature review and justified gap with requirements, design, implementation and evaluation of a bounded solution. The non-technical track investigates practices, experiences or organisational conditions through a systematic study of existing systems, documents or scenarios, without requiring implementation. Choose one track and agree the final research question, degree scope and contribution with the supervisor.
Programme fit
Information Systems. These are suggested research approaches, not a statement of confirmed programme policy. Agree the final title, track, degree scope and contribution with the supervisor and programme.
Shared research foundation
Review the literature; identify and justify a gap; formulate research questions; conduct a systematic study; analyse the evidence; explain the contribution relative to prior research and discuss limitations. The technical track additionally includes requirements, design, implementation and evaluation of an artifact.
